Journal Article

A heuristic algorithm for solving the minimum sum-of-squares clustering problems

Bagirov, Adil M., Ordin, Burak

2015

Overview

Clustering is an important task in data mining. It can be formulated as a global optimization problem which is challenging for existing global optimization techniques even in medium size data sets. Various heuristics were developed to solve the clustering problem. The global k -means and modified global k -means are among most efficient heuristics for solving the minimum sum-of-squares clustering problem. However, these algorithms are not always accurate in finding global or near global solutions to the clustering problem. In this paper, we introduce a new algorithm to improve the accuracy of the modified global k -means algorithm in finding global solutions. We use an auxiliary cluster problem to generate a set of initial points and apply the k -means algorithm starting from these points to find the global solution to the clustering problems. Numerical results on 16 real-world data sets clearly demonstrate the superiority of the proposed algorithm over the global and modified global k -means algorithms in finding global solutions to clustering problems.

• Publisher: Springer US,Springer,Springer Nature B.V
• Subject: Algorithms / Cluster analysis / Clustering / Clusters / Computer Science / Data mining / Datasets / Experiments / Heuristic / Integer programming / Mathematical analysis / Mathematical models / Mathematical optimization / Mathematics / Mathematics and Statistics / Operations Research/Decision Theory / Optimization / Optimization algorithms / Optimization techniques / Real Functions / Studies / Tasks / Texts / Variables